Why Belgium? Rain, beer and data scientists!

I was planning on writing this blog for a while: not simply as an ode to my adopted (or is that adopting? I’m bold enough to think the feeling is kind of mutual ☺) country, but to offer some real thoughts on why there are so many fantastic data scientists in Belgium. When the recent tragedies in Brussels again put Belgium in the limelight for all the wrong reasons, it was the final kick up my backside to get it done.

So people often ask me: Why Belgium?

Why does an American choose to live in Belgium? It’s rainy like 450 days a year. You need at least three languages to have an informed stroll through the biggest city, Brussels. Worst of all, it’s a country that just can’t decide whether it is North European (re: cold, logical, efficient) or South European (re: warm, passionate, slow). The funny thing is it’s mostly Belgians who repeatedly point these things out and ask me those questions.

Well, it’s precisely those three things that make Belgium so wonderful, and a wonderful breeding ground for data scientists (And yes, data scientists do breed, contrary to popular belief that they are created in laboratory test tubes).

Rain

Let’s start with the rain. Well yeah, it gets me down at times. But it also encourages me to think, and to reconsider, and to think again. The Belgian rain somehow instills an insatiable desire to learn, to innovate and to make things better. To optimize both the time indoors and especially those fleeting moments when the sun does peek its head out. How the hell do you think Belgian chocolate and beer got so fantastic (OK, got me, there’s more reasons to love Belgium)?

Precisely that drive to learn is the single most important characteristic of a good data scientist. Sure they need to find the data, analyze it, make sense of it and share the results. But if they don’t ask good questions first and foremost, they’re not going to make much of all the data or statistic skills in the world. So yes, rain makes good data scientists. I’m not proving correlation, I’m boldly stating causation!

Languages

Of course just asking questions and finding answers is not enough. Data scientists need to be able to communicate their findings in a simple language in which others will find insights and inspiration. In learning additional languages, people learn how to break communication down to its core. To say the very most with the very least number of words. Learning to speak another language is an extreme form of empathy: not simply understanding but actively communicating in the completely different paradigm of not just another person, but a whole other country or region.

So yes, learning languages, and not JUST computer languages makes better, more human than human data scientists. Belgium has three national languages (Dutch, French and German) and the politically correct solution for multi-lingual meetings is often English. On top of that most Belgians speak some form of dialect on top of their ‘standard’ language. So yeah, Belgians learn a lot of languages.

And if you learn languages in a rainy country, watch out!

North-South

But what about this North-South thing? Sure, the precise, logical, hard-working mindset associated with the stereotype Northern European is typically more conducive to better data science. That said, I believe that we are coming to a genuine technological and societal crossroads. Innovation and economy of scale is reaching a point where everyone needing to work themselves silly to sustain themselves is just no longer relevant. Everyone needs a purpose in life, but that purpose no longer needs to be a nine-to-fiver. We need to seek balance between growth and harmony given the limited resources of the Earth. Understanding that life is a constant balance: Growth and harmony, work and rest, production and art, black and white is also a key to finding real answers to the big questions. The best data scientists know there are answers in data, but just as many answers in books, in music, in food, in helping others.

OK, maybe you think I’m getting a bit lost here. But I do really think that Belgians generally try to strike the necessary balance between north and south European cultures. As a result, in my experience, Belgian data scientists tend to lead incredibly rich lives. I’m privileged to work in Belgium with a variety of data scientists who possess this ability to conduct balanced analyses, and then meet me at the pub afterwards to discuss it over a Trappist ale.

Want some examples of the great data scientists in Belgium?

How about my favorite sparring partner for how to really make analytics WORK in the organization? Geert Verstraeten co-founded Python Predictions who are absolutely world-class experts in getting the analytics job done. Sure they’ll collect the data and do the math, but more importantly, they’ll operationalize the process and make sure your organization internalizes it.

How about my partner in crime in the very first analytics prototype done at Colruyt, the Belgian retailer who have pushed analytics into all corners of the organization? Manuel Piette now leads a crack team of ten analysts and BNP Paribas and still somehow manages to find time between expert coaching to evaluate the latest Python libraries for relevance in the existing and potential BNP marketing analytics pipelines.

How about the man who seems to tear down the walls between academia and industry on a world-wide quest for deeper analytics use and comprehension ever? Bart Baesens is the stat-guru who makes complex algorithms digestible and useful for a wide range of statistically-traumatised business users.

And how about global expert on competitive analytics in the wafer-thin profit line retail sector? Maaike Van Den Branden has worked at Dun Humby, Delhaize, and now Aimia on everything from customer cross-sell propensity scoring through to SKU level product forecasting.